Abstract
The key point on analyzing the data stream measured by fiber optic distributed acoustic sensing (DAS) is signal activity detection separating measured signals from environmental noise. The inability to calculate the threshold for signal activity detection accurately and efficiently without affecting the measured signals is a bottleneck problem for current methods. In this article, a novel signal activity detection method with the adaptive-calculated threshold is proposed to solve the problem. With the analysis of the time-varying random noise’s statistical commonality and the short-term energy (STE) of real-time data stream, the top range of the total STE distribution of the noise is found accurately for real-time data stream’s ascending STE, thus the adaptive dividing level of signals and noise is obtained as the threshold. Experiments are implemented with simulated database and urban field database with complex noise. The average detection accuracies of the two databases are 97.34% and 90.94% only consuming 0.0057 s for a data stream of 10 s, which demonstrates the proposed method is accurate and high efficiency for signal activity detection.
Highlights
The advanced perception technology is the source of big data, the foundation of artificial intelligence development, and the key technical support to construct a smart earth, a smart ocean and a smart city [1]
The results demonstrate that the proposed methods meet the application requirements of distributed acoustic sensing (DAS) for signal activity detection
The performance of the proposed method is compared with two representative detection methods: the longterm spectral flatness measure (LSFM) [21] and the short-time Fourier transform (STFT) methods [26]
Summary
The advanced perception technology is the source of big data, the foundation of artificial intelligence development, and the key technical support to construct a smart earth, a smart ocean and a smart city [1]. It is urgent to develop a low computational complexity, fast response, high accuracy and strong robustness signal activity detection for real-time signal analysis. A real-time signal detection based on an STE crossing level algorithm with an average accuracy of 84.4% was implemented in [18], and a dual-threshold method combined by STE and ZCR with an average accuracy of 76.45% was presented in [19]. Both predefined thresholds are set by the environmental noise at the initial moment, which leads to inaccurate detection for the varying noise
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